🚀 I’m pleased to announce a new preprint! "SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities" 📢 Check it out & contribute! 📜 Paper: arxiv.org/abs/2407.11676 💻 Code: github.com/scikit-adapt...

🚀 Meet skore—the @scikit-learn.org sidekick! 🔹 Offers guidance on modeling 🔹 Automated reports & key metrics 💡 Built by scikit-learn maintainers. Open-source & ready to use! Try it 👉 tinyurl.com/bdhszwtn

With some help (and terrifying but so nice code reviews) from @agramfort.bsky.social we made it a proper Python library with a documentation and an example gallery. pythonot.github.io/auto_example...

Great science and friends moments this year at @neuripsconf.bsky.social see you next year 👋

I will be at @neuripsconf.bsky.social 2024 in Vancouver next week. Ping me if you want to grab ☕️ or🍺 and chat about ML stuff for biosignals (EMG, EEG, MEG, …)

Next week, we’ll present our spotlight paper at #NeurIPS2024 on domain adaptation for EEG data. Join us in East Exhibit Hall A-C on Friday at 4:30 PM! arxiv.org/abs/2407.03878 Apolline Mellot @sylvchev.bsky.social @agramfort.bsky.social @dngman.bsky.social A thread: 1/7

Anne Gagneux, Ségolène Martin, @quentinbertrand.bsky.social Remi Emonet and I wrote a tutorial blog post on flow matching: dl.heeere.com/conditional-... with lots of illustrations and intuition! We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423